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- W4375798913 abstract "As learning-based methods make their way from perception systems to planning/control stacks, robot control systems have started to enjoy the benefits that data-driven methods provide. Because control systems directly affect the motion of the robot, data-driven methods, especially black box approaches, need to be used with caution considering aspects such as stability and interpretability. In this letter, we describe a differentiable and hierarchical control architecture. The proposed representation, called <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>multi-abstractive neural controller</i> , uses the input image to control the transitions within a novel discrete behavior planner (referred to as the visual automaton generative network, or <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>vAGN</i> ). The output of a vAGN controls the parameters of a set of dynamic movement primitives which provides the system controls. We train this neural controller with real-world driving data via behavior cloning and show improved explainability, sample efficiency, and similarity to human driving." @default.
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- W4375798913 date "2023-08-01" @default.
- W4375798913 modified "2023-09-29" @default.
- W4375798913 title "Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving" @default.
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- W4375798913 doi "https://doi.org/10.1109/lra.2023.3273421" @default.
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